Dispatch Of Microgrid Research Articles

Dispatch for a Continuous-Time Microgrid Based on a Modified Differential Evolution Algorithm

The carbon trading mechanism is proposed to remit global warming and it can be considered in a microgrid. There is a lack of continuous-time methods in a microgrid, so a continuous-time model is proposed and solved by differential evolution (DE) in this work. This research aims to create effective methods to obtain some useful results in a microgrid. Batteries, microturbines, and the exchange with the main grid are considered. Considering carbon trading, the objective function is the sum of a quadratic function and an absolute value function. In addition, a composite electricity price model has been put forward to conclude the common kinds of electricity prices. DE is utilized to solve the constrained optimization problems (COPs) proposed in this paper. A modified DE is raised in this work, which uses multiple mutation and selection strategies. In the case study, the proposed algorithm is compared with the other seven algorithms and the outperforming one is selected to compare two different types of electricity prices. The results show the proposed algorithm performs best. The Wilcoxon Signed Rank Test is also used to verify its significant superiority. The other result is that time-of-use pricing (ToUP) is economic in the off-peak period while inclining block rates (IBRs) are economic in the peak and shoulder periods. The composite electricity price model can be applied in social production and life. In addition, the proposed algorithm puts forward a new variety of DE and enriches the theory of DE.

Low-carbon economic dispatching of microgrid considering generalized integrated demand response and nonlinear conditions

With the depletion of fossil fuels and the deterioration of the ecological environment, it is crucial to realize the low-carbon economic dispatching of microgrids (MG). In previous studies, the Demand Response (DR) can reduce microgrid operating costs and CO2 emissions by optimizing the load curve, under this background, this paper considers the coupling relationship between electricity, heat, and gas. And by the introduction of Power-to-Gas (P2G) and Organic Rankine Cycles (ORC), the flexible conversion of electricity-gas and heat-electricity is realized. In addition, based on the characteristics of the time-shifting load, interruptible load, and alternative load, the Generalized Integrated Demand Response (GIDR) model is constructed. Secondly, to restore the non-linear status of energy conversion equipment efficiency under different partial loads, this paper adopts data-driven modeling and uses Multilayer Perceptron (MLP) to fit the curves of key parameters, with the help of the Soft Actor–Critic (SAC) algorithm, the solution of the model is realized. In the case study section, this paper verifies the rationality of the proposed method by using real data from Northwest China. The results show that GIDR can reduce the cost by 16.40% and reduce the CO2 emissions by 32.26%, and compared with not considering nonlinear conditions, the method in this paper can reduce the operating cost by 23.16% and carbon emissions by 33.00%.

Economic dispatch in micro-grids with alternative energy sources and batteries

The problem of economic dispatch in a micro-grid interconnected to the conventional electrical system consists of optimizing the operating cost to satisfy the different operating requirements and the demand for electrical power required by the users. In the development of this proposal, there are challenges caused by the random nature of electricity generation systems based on renewable energy sources, for an adequate operation of the micro-grid, which can be solved through systems Energy Storage System (BESS). This paper addresses the problem of optimal economic dispatch in a microgrid that includes an energy storage system based on a battery bank and interconnected to the main grid through a mathematical programming approach, considering the benefits for customers by reducing their demand of electrical power and taking into account the costs of treatment pollutants produced by conventional energy sources. The generation systems used to satisfy the energy demand of the customers within the interconnected micro-grid are: a) conventional diesel generators, b) wind generation systems, c) photovoltaic systems, d) a hydroelectric turbine, and e) a storage system based on electric batteries. The results of the effectiveness of the proposed mathematical model show that the demand for electrical power to the main grid can be reduced and customers obtain economic benefits by integrating the incentive-based demand response program into the energy management problem when environmental contingency events occur. The results show that when there are peaks in energy demand, the operating costs of the micro-grid increase by up to 380%. In the same way, when there are failures in renewable energy sources, storage systems (BESS) and diesel generators work at 100% of their capacities. The proposal is implemented in Lingo 17.0 software and simulation results show the feasibility of the proposal through the optimization of the operation of the micro-grid.

PBI Based Multi-Objective Optimization via Deep Reinforcement Elite Learning Strategy for Micro-Grid Dispatch With Frequency Dynamics

Micro-grid dispatch is facing a great challenge since the system security and economic cost are often time-varying with different requirements. In this paper, a novel multiple time-scale dispatch model is firstly constructed with considering load voltage and frequency dynamics of islanding awareness, which is limited with rate-of-change of frequency (RoCoF) of micro-grid. Based on this model, micro-grid power dispatch is converted into a multi-objective optimization problem (MOP) with taking economic cost, voltage deviation and frequency stability into consideration. To address this problem, a penalty-based boundary intersection (PBI) based multi-objective optimization approach is developed with an elite learning technique. In the above approach, a developed deep deterministic policy gradient (DDPG) is proposed to learn evolutionary parameters with grid based alternating weight vectors, which can lead to better convergence ability and diversity distribution. Simulation results show that the proposed optimization strategy can properly achieve minimum economic cost with simultaneously satisfying voltage and frequency stability, which provides a viable and promising way for tackling with micro-grid dispatch problem.

Coordinated optimal dispatch of composite energy storage microgrid based on double deep Q-network

Coordinated optimal dispatch of composite energy storage microgrid based on double deep Q-network

Hybrid optimisation strategy-based economic emission dispatch for microgrid

Hybrid optimisation strategy-based economic emission dispatch for microgrid

Privacy-Preserving Distributed Economic Dispatch of Microgrids: A Dynamic Quantization-Based Consensus Scheme With Homomorphic Encryption

This paper is concerned with the privacy-preserving distributed economic dispatch problem (ED) of microgrids. A homomorphically encrypted consensus algorithm is developed in the absence of a third party to achieve optimal power distribution with the least cost while preventing sensitive information leakage during the entire communication process. For ease of data encryption, a novel estimator-like dynamic quantizer is first constructed, where the information to be transmitted is converted into a series of finite-level codewords. Then, a sufficient condition is derived, taking advantage of mathematical induction and the properties of matrix norms, to ensure that the quantization output is unsaturated and exact consensus is reached. Furthermore, by means of the additive homomorphic property of the Paillier algorithm to embed secrecy in pairwise interaction dynamics, the confidential communication strategy is adopted to ensure that the distributed algorithm converges to the optimal value without disclosing private or sensitive state information of agents. Finally, case studies are provided to illustrate the feasibility and validity of the adopted privacy-preserving ED scheme in IEEE 39-bus power systems.

Application of adaptive mutation bird swarm algorithm in optimal dispatching of microgrid grid connection

Distributed power grid integration contributes to both the reduction of greenhouse gas emissions and the protection of the environment. Nevertheless, the uncertainty and volatility associated with the production of clean renewable energy adds additional challenges to microgrid dispatch. The paper presents an adaptive mutant bird swarm algorithm and suggests a comparison mechanism based on population fitness variances and optimal values in order to overcome the shortcomings of BSA, in particular its tendency to self-correct into local optimum and slow convergence speed. First, the algorithm determines if the population is in the local optimal state. The local optimal individual is then subjected to Cauchy mutation in order to determine the optimal value again. This improves the accuracy and speed of the BSA. Based on simulation results, the improved algorithm has higher optimization accuracy and faster optimization speed, which demonstrates the effectiveness and advancement of the algorithm proposed in this research.

Impact of electric vehicles on optimal power dispatch of a micro-grid in competitive electric market

ABSTRACT In this study, for optimal power dispatch of a grid-connected micro-grid, a new stochastic model has been built up to minimize the operating cost of micro-grid that equipped with plug-in hybrid electric vehicles, renewable energy sources, and storage devices. Impact of electric vehicles on power dispatch is studied by considering its uncertainty charging characteristics. Monte–Carlo simulation is employed for uncertainty modeling. For this work, three different charging strategies are followed up, namely, uncontrolled, controlled, and smart charging strategy to observe the impact of electric vehicles on micro-grid. So, here an endeavor has been made using a potent and robust technique i.e. improved whale optimization algorithm for obtaining optimal power dispatch. The suggested method’s appropriateness and effectiveness are evaluated by modeling a grid-connected micro-grid. The outcomes of this technique improve the MG’s performances in terms of best solution and economic operation. On different case studies, the outcomes are compared with other methods without and with charging strategy of electric vehicles. It is seen that operating cost obtained without electric vehicles is nearly 300$ and with charging strategy, i.e. uncontrolled, controlled, and smart of electric vehicles are 664$, 390$, and 327$, respectively. So, inclusion of vehicles on MG increases operating cost but its impact on micro-grid significantly reduces the operating cost for smart charging strategy in comparison with other charging strategy.

Flexible Transfer Converters Enabling Autonomous Control and Power Dispatch of Microgrids

For grid-connected or interconnected microgrids, the transition to an autonomous and decentralized architecture was hampered by two perplexing issues: how to guarantee voltage-supporting capacity, while fully and controllably utilizing local power generation, and how to switch between islanded and grid-connected modes flexibly and seamlessly. Through a combination of benefits of interlinking converters and switches, the flexible transfer converters (FTCs), positioned at the interfaces between the utility grid and microgrids, are offered as a solution in this article. The basic concepts and classifications of the FTC are first presented, and a general-purpose FTC and a universal control strategy for distributed generations are then proposed as an example, which contribute to appropriate power dispatch and flexible mode transfer in a communication-free design, leading to a fully autonomous microgrid. Simple and economic operation rules, as well as high interoperability with existing assets, makes this solution promising for distributed microgrid applications. Further, generalized small-signal models of the target system are derived for stability analysis and parameter design. Finally, comprehensive case studies in simulations and experiments are provided to validate its effectiveness.

Weighted matrix based distributed optimization method for economic dispatch of microgrids via multi-step gradient descent

Due to the slow convergence rates and reliance on global information, distributed optimization methods, such as alternating direction method of multipliers (ADMM) and distributed gradient descent method (DGDM), have been difficult to meet the needs of solutions for the large-scale distributed system. In this paper, a distributed multi-step gradient descent method (DMGDM) combined with the allocation of upper bounds of second derivatives, has been proposed to tackle the economic dispatch problem (EDP) of microgrid (MG). At first, the agent assigns the reciprocal of its own upper bound of the second derivative according to the product of out-degree and upper bounds of second derivatives from the neighbors, which is the key to our method. Further, the weight matrix is constructed through the negotiation among neighbors in a distributed manner. Additionally, the distributed weight matrix relaxes the convergence conditions of the proposed method so that momentum parameters can be tuned without the need for global information. Numerical examples show that the convergence rate of our method is faster than the ADMM and the DGDM. Finally, a bi-layer optimization model of the EDP of MG is built via Matlab/Simulink, and the results show that the proposed method can realize the optimal dispatch of controllable distributed generators (DGs) in the MG.

A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching

Rule-based micro-grid dispatch strategies have received significant attention over the last two decades. However, a recent body of literature has conclusively shown the benefits of operational scheduling optimisation while optimally sizing micro-grids. This is commonly referred to as micro-grid design and dispatch co-optimisation (MGDCO). However, as far as can be ascertained, all the existing MGDCO models in the literature consider a 24-h-resolved day-ahead timeframe for the associated optimal energy scheduling processes. That is, intelligent, look-ahead energy dispatch strategies over multi-day timeframes are generally absent from the wider relevant literature. In response, this paper introduces a novel MGDCO modelling framework that integrates an arbitrage-aware linear programming-based multi-day energy dispatch strategy into the standard metaheuristic-based micro-grid investment planning processes. Importantly, the model effectively extends the mainstream energy scheduling optimisation timeframe in the micro-grid investment planning problems by producing optimal dispatch solutions that are aware of scenarios over three days. Based on the numeric simulation results obtained from a test-case micro-grid, the effectiveness of the proposed optimisation-based dispatch strategy in the micro-grid sizing processes is verified, while retaining the computational tractability. Specifically, comparing the proposed investment planning framework, which uses the formulated 72-h dispatch strategies, with the business-as-usual MGDCO methods has demonstrated that it can reduce the micro-grid’s whole-life cost by up to 8%. Much of the outperformance of the proposed method can be attributed to the effective use of the behind-the-meter Li-ion battery storage, which improves the overall system flexibility.

Scenario-Based Stochastic Optimization for Energy and Flexibility Dispatch of a Microgrid

Energy storage is one of the most important components of microgrids with non-dispatchable generators and can offer both energy and flexibility services when the microgrid operates in grid-connected mode. This paper proposes a scenario-based stochastic optimization model that can be used to determine the energy and flexibility dispatch of a residential microgrid with solar and stationary battery systems. The objective of the model is to minimize the expected energy and peak power cost as well as the battery aging cost, while maximizing the expected revenue from flexibility. The formulated stochastic optimization problem is solved in rolling horizon with the uncertainty model being dynamically updated to consider the most recent forecast profiles for solar power and electricity demand. The benefits of the proposed approach were demonstrated by simulating the daily operation of a real building. The results showed that the estimated flexibility was successfully dispatched yielding an economic value of at least 7% of the operation cost of the building microgrid. The model can be used by flexibility providers to assess their flexibility and design a bidding strategy as well as by system operators to design incentives for flexibility providers.

Economic dispatch of an islanded microgrid

Microgrids are increasingly becoming popular to improve energy access and increase the resiliency of weakly connected rural networks. The economic operation of these microgrids with renewable energy resources is key for maximizing the benefits. The objective of this paper is to implement the economic dispatch of a microgrid using quadratic programming, considering the active and reactive power capability of the renewable energy resources. The open distribution system simulator (OpenDSS) is used for obtaining the load flow of the distribution network, and the converged solution is given as input to MATLAB for optimization. The simulation is carried out for one day with a variation of hourly load, solar PV radiation, and wind in both grid-connected and islanded modes under six different cases. The methodology is implemented on a modified IEEE-13 node test feeder distribution system. The simulation result shows that the microgrid with distributed generation (DG) capable of supplying both active and reactive power under islanded conditions is economical when compared to the grid-connected mode. The novelty of the work is that it considers the capability of distributed generation to supply both active and reactive power. That can make it fully autonomous as it can meet the hourly load requirement of the network under islanding mode as per IEEE Standard 1547.4.

Optimal economic dispatch of hybrid microgrids integrating Energy Storage Systems with Grid-Forming Converters

New control strategies based on Grid-Forming converters make possible the operation of isolated microgrids without the support of synchronous generation. This paper proposes an Energy Management System algorithm that optimizes the operation of a microgrid, maximizing the integration of renewable energy and considering the possibility of disconnecting all synchronous generators. The algorithm is applied to a microgrid modelled with real data and two economic dispatch strategies are analysed: the first requires keeping at least one synchronous generator connected to provide frequency regulation following standard practice; and the second considers the disconnection of all conventional generation relying on the capabilities of Grid-Forming converters when sufficient reserve is available. The results show that the proposed strategy reduces the operational cost of the system, as well as solar PV curtailment and diesel consumption.

False Data Injection- and Propagation-Aware Game Theoretical Approach for Microgrids

Defending microgrids against cyberattacks has been recognized as a significant task in modern energy systems. This paper studies a False Data Injection Attack (FDIA) scenario targeting microgrids, in which the attacker maliciously propagates false data malware in the communication network of a microgrid system, aiming at misleading the microgrid’s operation. Firstly, this paper establishes the propagation and attack models for the false data malware; then, the impact of the FDIA is quantified through the formulation of a network-constrained microgrid dispatch model. Based on this, a false data malware propagation-aware game between the attacker and the defender is established and an effective computational approach is developed for solving the proposed game and obtaining the potential Nash equilibrium strategy pair. Extensive numerical simulations are conducted to validate the proposed framework.

Fast distributed gradient descent method for economic dispatch of microgrids via upper bounds of second derivatives

Distributed optimization algorithms, such as alternating direction method of multipliers (ADMM) and distributed gradient descent method (DGDM), can no longer meet the needs of the application in the large system due to their slow convergence rates. In this paper, based on the upper bounds of second derivatives, a fast distributed gradient descent method (FDGDM) has been proposed, where the upper bounds of second derivatives and out-degree information are exchanged among the neighbors, and the weight matrix used for iteration is completely distributedly formed. Especially, the objective function keeps declining monotonically during the iterative process, which is the key to accelerating the convergence rate of our method while maintaining the constraints. Numerical experiments are designed to verify the faster convergence rate of the FDGDM compared with the ADMM. Finally, a microgrid (MG) simulation platform based on Matlab/Simulink is established, and our method is applied to solve the economic dispatch problem (EDP) of MG. The results demonstrate that the proposed method can achieve the optimal economic dispatch while ensuring the operation stability.

Collaborative optimal dispatch of microgrid and electric vehicles based on the Stackelberg game

The development of both microgrids and electric vehicles has become an important part of the current energy scenario. Useful complementary advantages can be formed between electric vehicles and microgrids, the consumers of which can utilize renewable energy and narrow the peak–valley differences of the net load curve while ensuring their own pecuniary interests. Based on the idea of the Stackelberg game, an optimal dispatch model of a microgrid with electric vehicles is proposed herein, where the benefits of the state of charge are taken into account. In the upper layer of the model, the charging and discharging behaviors of electric vehicles are guided by the goal of minimizing the operating cost of the microgrid. In the lower layer of the model, electric vehicle users adjust the charging and discharging strategies with the goal of maximizing their individual interests. The study results demonstrate that the proposed model not only reflects the benefits of both the master and slave but also reduces the peak–valley differences of the microgrid load. Further, the charging and discharging times of electric vehicles are reduced, and their state of charge is maintained at a high level.

An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids

To overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies.

CHP-Based Economic Emission Dispatch of Microgrid Using Harris Hawks Optimization

In this paper, the economically self-sufficient microgrid is planned to provide electric power and heat demand. The combined heat and power-based microgrid needs strategic placement of distributed generators concerning optimal size, location, and type. As fossil fuel cost and emission depend mainly on the types of distributed generator units used in the microgrid, economic emission dispatch is performed for an hour with a static load demand and multiple load demands over 24 h of a day. The TOPSIS ranking approach is used as a tool to obtain the best compromise solution. Harris Hawks Optimization (HHO) is used to solve the problem. For validation, the obtained results in terms of cost, emission, and heat are compared with the reported results by DE and PSO, which shows the superiority of HHO over them. The impact of renewable integration in terms of cost and emission is also investigated. With renewable energy integration, fuel cost is reduced by 18% and emission is reduced by 3.4% for analysis under static load demand, whereas for the multiple load demands over 24 h, fuel cost is reduced by 14.95% and emission is reduced by 5.58%.